A method and system for early warning analysis of Alzheimer's disease based on multiple Chinese syllables

Abstract

The embodiments of the present disclosure provide an Alzheimer's disease early warning analysis method and system based on multiple Chinese syllables. Applied to the field of data processing technology, it includes: collecting speech data of Alzheimer's patients and performing data perception to construct polysyllabic samples; using a multi-level graph embedding sparse feature learning method to preprocess data to obtain polysyllabic speech output features; constructing a classifier, performing feature optimization processing on the output features, and outputting a cost-sensitive matrix; constructing a cost-sensitive weighted objective function based on the cost-sensitive matrix, and calculating the polysyllabic fusion analysis diagnosis results respectively, and predicting the probability of Alzheimer's disease based on the diagnosis results; and providing guidance and suggestions to patients based on the probability prediction results. In this way, the present invention uses concise Chinese syllables to collect patient data, and combines the polysyllabic fusion analysis method to obtain an early warning scheme for Alzheimer's disease, which is conducive to improving the accuracy of early warning analysis.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a multi-Chinese syllable-based Alzheimer's disease early warning analysis method and system. Background Art

[0002] Alzheimer's disease (AD) is a progressive neurological disorder characterized by progressive cognitive impairment. It causes the death of neurons and the breakdown of connections in the brain, affecting memory, thinking, language, and behavior. Patients may experience memory loss, become lost, lose recognition of loved ones, and experience decreased language skills, ultimately leading to severe cognitive impairment and the inability to function independently.

[0003] Currently, there are numerous methods for assessing and diagnosing Alzheimer's disease, and their combined application can accurately diagnose a patient's cognitive impairment and provide a strong basis for subsequent treatment. However, these methods are affected by factors such as the subject's educational level and emotional state, and are also costly, complex, and unsuitable for routine screening. Furthermore, some methods are invasive and unsuitable for current Alzheimer's disease diagnostic practice.

[0004] Early warning of Alzheimer's disease based on speech analysis has the advantages of being non-invasive, convenient, low-cost, and objective. Therefore, it is urgently needed to provide a method and corresponding system for diagnosing and predicting Alzheimer's disease based on speech data. Summary of the Invention

[0005] The present disclosure provides an Alzheimer's disease early warning analysis method and system based on multiple Chinese syllables. By adopting concise Chinese syllables to collect patient data and combining it with a multi-syllable fusion analysis method, it solves the technical problems that the existing technology cannot effectively and accurately diagnose patient cognition and has poor prediction results due to multiple factors affecting patient voice data.

[0006] According to a first aspect of the present disclosure, a method for early warning analysis of Alzheimer's disease based on multiple Chinese syllables is provided, comprising the following steps:

[0007] Collect speech data from Alzheimer's patients and perform data perception to construct polysyllabic samples;

[0008] Using a multi-level graph embedding sparse feature learning method to perform data preprocessing on the polysyllabic sample to construct a polysyllabic speech output feature;

[0009] Constructing a classifier, using a cost-sensitive random configuration network in the classifier to perform feature optimization processing on the polysyllabic speech output features, and outputting a cost-sensitive matrix;

[0010] A cost-sensitive weighted objective function is constructed based on the cost-sensitive matrix, multi-syllable fusion analysis diagnosis results of different classifiers are calculated respectively, and the probability of Alzheimer's disease is predicted according to the diagnosis results;

[0011] Provide guidance and advice to the patient based on the Alzheimer's disease probability prediction results.

[0012] According to the above aspects and any possible implementation, a further implementation is provided, wherein the process of collecting speech data of Alzheimer's patients and performing data perception to construct a polysyllabic sample is as follows:

[0013] A recording sample is prepared, and the recording sample is annotated with Chinese characters, Chinese syllables, international phonetic symbols, and pronunciation prompts according to Chinese syllables. Alzheimer's patient voice data is collected based on the annotated results to obtain a polysyllabic sample, wherein the polysyllabic sample includes syllable pronunciation data.

[0014] According to the above aspects and any possible implementation, a further implementation is provided, wherein the process of performing data preprocessing on the polysyllabic sample using the multi-level graph embedding sparse feature learning method to construct the polysyllabic speech output features is as follows:

[0015] Extracting features from the syllable pronunciation data to obtain Mel-Cep coefficients, timing errors, and amplitude perturbation speech features, and performing normalization processing to obtain processed speech features;

[0016] A graph random sparse autoencoder based on random configuration is constructed, and the graph random sparse autoencoder is used to extract the processed speech features to obtain polysyllabic speech output features.

[0017] According to the above aspects and any possible implementation, an implementation is further provided, wherein the process of constructing a graph random sparse autoencoder, extracting the processed speech features using the graph random sparse autoencoder, and obtaining the polysyllabic speech output features is as follows:

[0018] Obtaining processed speech features, extracting the processed speech features using the graph random sparse autoencoder, and obtaining a hidden layer node output matrix of the graph random sparse autoencoder;

[0019] Defining a graph random sparse autoencoder weight objective function, and taking the hidden layer node output matrix as input of the weight objective function, and outputting a graph random sparse autoencoder weight value;

[0020] The multi-syllabic speech output feature is constructed by combining the hidden layer node output matrix and the weight value.

[0021] According to the above aspects and any possible implementation, an implementation is further provided, wherein the process of constructing a classifier, performing feature optimization processing on the polysyllabic speech output features using a cost-sensitive random configuration network in the classifier, and outputting a cost-sensitive matrix is as follows:

[0022] Initializing a cost-sensitive matrix based on the multi-syllabic speech output feature;

[0023] A classifier is constructed using a cost-sensitive random configuration network based on an objective function, and the initialized cost-sensitive matrix is updated and outputted using the classifier and an intelligent optimization method to obtain a maximized objective function;

[0024] The cost-sensitive matrix corresponding to the maximized objective function is used as an output result.

[0025] According to the above aspects and any possible implementation, an implementation is further provided, wherein the maximized objective function is specifically:

[0026] ;

[0027] in, represents the objective function of classifier i, is the hidden layer output of classifier i, Represents the classification label, represents the classifier regularization parameter, is the cost-sensitive matrix, is the number of classifiers, is the number of cost-sensitive matrices.

[0028] According to the above aspects and any possible implementation, an implementation is further provided, wherein the process of constructing a cost-sensitive weighted objective function based on the cost-sensitive matrix, respectively calculating the multi-syllable fusion analysis diagnosis results of different classifiers, and predicting the probability of Alzheimer's disease based on the diagnosis results is as follows:

[0029] Constructing a weighted output matrix according to the cost-sensitive matrix and the hidden layer node output matrix;

[0030] Constructing a cost-sensitive weighted objective function according to the weighted output matrix and the maximization objective function, and calculating a fusion output matrix according to the cost-sensitive weighted objective function;

[0031] Calculating a diagnosis result based on the weighted output matrix and the fusion output matrix;

[0032] The diagnostic results are analyzed using a voting method to obtain a fusion analysis diagnostic result.

[0033] According to the above aspects and any possible implementation, an implementation is further provided, wherein the objective function based on cost-sensitive weighting is specifically:

[0034] ;

[0035] in, represents the original output weight of classifier k, is the weighted output matrix, specifically , is the weighted output matrix of classifier k, is the hidden layer output of classifier k, represents the regularization parameter of the fusion objective function, K is the number of classifiers, is the cost-sensitive matrix, B is the fusion output matrix, specifically , is the value of the cost-sensitive weighted objective function.

[0036] According to the above aspects and any possible implementation, a further implementation is provided, wherein the process of analyzing the diagnosis results using a voting method to obtain a fusion analysis diagnosis result is as follows:

[0037] Calculating the probability values of the diagnosis results respectively, wherein the probability values include two categories: Alzheimer's disease probability value and non-Alzheimer's disease probability value;

[0038] Determine the probability values of Alzheimer's disease and non-Alzheimer's disease in each diagnosis result, and vote for the category with the larger probability value;

[0039] Count the votes and use the category with the most votes as the output value of the fusion analysis diagnosis result.

[0040] According to a second aspect of the present disclosure, a multi-syllable-based Alzheimer's disease early warning analysis system is provided, which is used to implement the multi-syllable-based Alzheimer's disease early warning analysis method as described in the embodiment of the first aspect. The system includes: a data perception module, a data preprocessing module, a classifier module, a multi-syllable fusion diagnosis module, and a warning screening and guidance suggestion module;

[0041] The data perception module is used to collect speech data of Alzheimer's patients and perform data perception to construct a polysyllabic sample;

[0042] The data preprocessing module is used to perform data preprocessing on the polysyllabic sample by adopting a multi-level graph embedding sparse feature learning method to construct a polysyllabic speech output feature;

[0043] The classifier module is used to construct a classifier, and uses the cost-sensitive random configuration network in the classifier to perform feature optimization processing on the polysyllabic speech output features, and outputs a cost-sensitive matrix;

[0044] The multi-syllable fusion diagnosis module is used to construct a cost-sensitive weighted objective function based on a cost-sensitive matrix, calculate the multi-syllable fusion analysis diagnosis results of different classifiers respectively, and predict the probability of Alzheimer's disease based on the diagnosis results;

[0045] The early warning screening and guidance suggestion module is used to provide guidance and suggestions to patients based on the diagnosis results of the multi-syllable fusion diagnosis module.

[0046] Compared with the prior art, the present invention has the following technical effects:

[0047] (1) Given that Chinese syllables consist of three parts, namely initials, finals and tones, and have a complex structure and multiple combinations, this provides a rich and concise source of information for the diagnosis of Alzheimer's disease based on speech analysis. The present invention analyzes the accuracy and fluency of initials, finals and tones during the pronunciation process of patients, and captures such subtle language changes to more carefully assess their cognitive function status. At the same time, in order to reduce the influence of various factors such as age, gender, cultural background, and dialect, the present invention uses Chinese characters, syllables, international phonetic symbols and pronunciation guidance to ensure the accuracy of the collected data;

[0048] (2) Due to the influence of pronunciation differences of single syllables, it is impossible to guarantee high warning accuracy. Therefore, in order to improve the reliability of early warning diagnosis of Alzheimer's disease, the present invention is based on an evolutionary cost-sensitive random configuration network as a base classifier and a cost-sensitive weighted negative correlation learning model as a multi-syllable fusion model. The evolutionary cost-sensitive base classifier is weighted by weighting the sample weights, which is conducive to overcoming the influence of sample data noise and uneven distribution of sample categories; the cost-sensitive weighted negative correlation learner can fuse the classification results of multiple base classifiers;

[0049] (3) The present invention can collect data through different terminals such as personal computers, home TVs, mobile phones, and tablets, analyze and process the data using cloud servers, and provide early warning monitoring results in real time, achieving significant improvements and breakthroughs in implementation methods, perception quality, friendly interaction, and health management.

[0050] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0052] Figure 1 A schematic flow chart of a method for early warning analysis of Alzheimer's disease based on multiple Chinese syllables according to an embodiment of the present disclosure is shown;

[0053] Figure 2 A schematic diagram of a data perception process of an Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to an embodiment of the present disclosure is shown;

[0054] Figure 3 A schematic diagram of a data preprocessing process of an Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to an embodiment of the present disclosure is shown;

[0055] Figure 4 A schematic diagram of a classifier processing process of an Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to an embodiment of the present disclosure is shown;

[0056] Figure 5 A schematic diagram of a multi-syllable fusion diagnosis process of an Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to an embodiment of the present disclosure is shown;

[0057] Figure 6 A schematic structural diagram of an Alzheimer's disease early warning analysis system based on multiple Chinese syllables according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0058] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] Alzheimer's Disease (AD) is a neurodegenerative disease with a slow progression and worsening over time. According to statistics, the 2024 Alzheimer's Disease Data Report in my country shows that there are nearly 17 million Alzheimer's patients in my country, making it the country with the largest number of Alzheimer's patients in the world. The disease causes gradual damage to the patient's neurons and their neural connections, and eventually dies from the disease or its complications. The early stage of AD is mild cognitive impairment (MCI). At this stage, patients have normal daily living abilities, but there is a progressive decline in cognitive function. From a treatment perspective, AD is irreversible and there are great difficulties in treatment, but if patients can be treated at the MCI stage, the onset of dementia can be effectively delayed.

[0061] Currently, the diagnosis of Alzheimer's disease mainly includes the following aspects:

[0062] 1) Cognitive assessment: Mini-Mental State Examination, Montreal Cognitive Assessment, Wechsler Adult Intelligence Scale, etc., can assess the patient's cognitive function, including attention, memory, language ability, executive function and other areas.

[0063] 2) Imaging examination: Through medical examinations such as cranial CT scan and MRI, check for intracranial lesions, movement disorders, etc., especially atrophy of the hippocampus and medial temporal lobe, which is more common in Alzheimer's disease.

[0064] 3) Laboratory tests: also include thyroid function tests, blood tests, etc. to rule out other possible causes of cognitive impairment.

[0065] 4) Cerebrospinal fluid examination: Detection of certain specific biomarkers in the cerebrospinal fluid, such as beta-amyloid protein 42 (Aβ42), tau protein, etc. Abnormal levels of these biomarkers can help diagnose Alzheimer's disease.

[0066] 5) Genetic testing: For patients with a family history or early-onset Alzheimer's disease, genetic testing may help identify the cause, such as testing for the APOE gene.

[0067] 6) Positron emission tomography: This uses specific tracers, such as fluorodeoxyglucose or amyloid tracers, to observe the metabolic activity and amyloid deposition in the brain. This is also an important means of diagnosing Alzheimer's disease.

[0068] The combined use of these methods can more accurately diagnose a patient's cognitive impairment and provide a strong basis for subsequent treatment. However, these methods: 1) are affected by factors such as the subject's educational level and emotional state; 2) through 6) are costly and complex, making them unsuitable for routine screening; and 3) some are invasive.

[0069] With the development of artificial intelligence (AI) technology, early warning systems for Alzheimer's disease (AD) based on speech analysis are being widely researched both domestically and internationally. To mitigate the influence of various factors, such as age, gender, cultural background, and dialect, on speech diagnosis, this paper uses concise Chinese syllables for patient data collection and combines them with a multisyllabic fusion analysis method. This provides a novel early warning system for Alzheimer's disease (AD) for Chinese speakers, potentially improving the accuracy of early warning analysis.

[0070] Reference Figure 1 As shown, this embodiment provides an Alzheimer's disease early warning analysis method based on multiple Chinese syllables, comprising the following steps:

[0071] S101. Collect speech data of Alzheimer's patients and perform data perception to construct a polysyllabic sample.

[0072] like Figure 2 As shown, in this embodiment, before recording the Chinese syllable sample, the Chinese characters, Chinese syllables, international phonetic symbols and pronunciation prompts of the corresponding syllables are given on any medium with an operating system, a microphone and a recording medium, such as a mobile phone, a tablet, a computer, a television, etc., to ensure the accuracy of the recording.

[0073] Specifically, the cognitive impairment caused by AD affects language expression ability, which is reflected in the process and content of language expression. Therefore, by collecting the user's voice (such as through the microphone of an electronic device) while performing a preset descriptive task to obtain voice information, an algorithm based on voice analysis can be used to test the user's language expression ability and then assess the degree of cognitive impairment, thereby detecting AD and MCI based on voice information.

[0074] The preset description task may include describing the content of a preset image and describing the names of different target objects of the target type within a preset time. It is understandable that impaired cognitive function will affect the accuracy of the description of the preset image. For the content in the image, the more severe the cognitive impairment, the worse the accuracy of the description. When performing the description task, the display screen of the electronic device can display the preset image. The accuracy of the description of the preset image by a normal user and a user with impaired cognitive function is obviously different. Similarly, impaired cognitive function will also affect the fluency of the description. For example, describing the names of different target objects of the target type within a preset time can be saying as many animal names as possible within a preset time (such as 30 seconds, 1 minute, 2 minutes, etc.). Therefore, by collecting voice information of the user performing the preset description task, it can be used to detect the degree of cognitive impairment, thereby detecting the probability of the user suffering from AD and MCI.

[0075] S102. Use a multi-level graph embedding sparse feature learning method to preprocess the data of the polysyllabic sample and construct a polysyllabic speech output feature.

[0076] like Figure 3 As shown in the example, for audio data of different syllables, speech features such as Mel-frequency cepstral coefficients (MFCC), time base error (Jitter), and amplitude perturbation (Shimmer) are extracted, and feature normalization is performed to obtain normalized preprocessing features, which are then input into multiple graph random sparse autoencoders for sparse feature learning and generate combined features. ,in, , Voice features.

[0077] Specifically, taking single-shot feature learning as an example, the learning process of the i-th graph random sparse autoencoder is as follows:

[0078] The graph random sparse autoencoder consists of Lmax nodes, assuming Nodes have been generated, and the hidden layer nodes are generated using the random configuration incremental learning method. Parameters, hidden layer nodes The output matrix is formula (1):

[0079] (1)

[0080] in, To extract the speech features of each training sample, N represents the number of samples; Represents the output of node L; and Represent the weight and bias of node L respectively; represents the activation function and T is the transposed matrix.

[0081] The weight objective function of the i-th graph random sparse autoencoder is defined as shown in formula (2), represents the weight of the i-th graph random sparse autoencoder, then the output of the i-th graph random sparse autoencoder is , specifically:

[0082] (2)

[0083] (3)

[0084] in, ; Tr represents the trace or trace number of the matrix; and represents the reconstructed sparse features of sample i and sample j, express and The similarity, and Represents a different pair of data samples, if and If they are neighbors, the weight coefficient is obtained. , represents the Gaussian kernel parameter, otherwise ; represents the graph Laplacian matrix, Depend on The composition of each element, represents a diagonal matrix; , represents the sparse weight regularization parameter; represents the graph regularization parameter.

[0085] S103: Construct a classifier, use the cost-sensitive random configuration network in the classifier to perform feature optimization processing on the multi-syllabic speech output features, and output a cost-sensitive matrix.

[0086] like Figure 4 As shown, in this embodiment, in order to reduce the impact of medical data sample noise and category imbalance on the classification results, taking classifier i as an example, the graph embedding sparse features of input pronunciation i, that is, the multi-syllabic speech output features, is used as the classifier using a cost-sensitive random configuration network based on the objective function of formula (4):

[0087] (4)

[0088] in, represents the objective function of classifier i; is the hidden layer output of classifier i; Represents a classification label; represents the classifier regularization parameter, is the number of classifiers.

[0089] Introducing cost-sensitive matrix , where i=1,2,3,…,k, The classifier is weighted and the cost-sensitive matrix is iteratively optimized and updated using an intelligent optimization algorithm until the maximum number of iterations of the intelligent optimization algorithm is met. The cost-sensitive matrix that achieves the maximum diagnostic accuracy is selected as the final matrix to improve the accuracy of Alzheimer's disease warning for a single utterance.

[0090] S104. A cost-sensitive weighted objective function is constructed based on the cost-sensitive matrix, and the multi-syllable fusion analysis diagnosis results of different classifiers are calculated respectively, and the probability of Alzheimer's disease is warned based on the diagnosis results.

[0091] like Figure 5 As shown, in order to achieve multi-syllable fusion diagnosis, the evolution cost sensitivity matrix of each syllable classifier is and its hidden layer output matrix , construct the weighted output matrix ,in , using formula (5) as the objective function, solve the fusion output matrix , specifically:

[0092] (5)

[0093] in, represents the original output weight of classifier k; ; represents the regularization parameter of the fusion objective function, is the cost-sensitive weighted objective function value, is the weighted output matrix of classifier k.

[0094] Finally, the diagnostic results are calculated respectively , using voting method to obtain fusion diagnosis results .

[0095] In this embodiment, a soft voting method is used to determine the fusion diagnosis result. Soft voting is an integrated strategy in multi-model voting, which is particularly suitable for classification problems. It is a probability-based voting mechanism in which the prediction result of each model is regarded as an estimate of a certain probability of the category. The final prediction result is determined based on the weighted average of the prediction probabilities of all models. The final voting result can often utilize the complementary functions between single-classification models to reduce the error of a single classifier and improve the prediction performance and classification accuracy.

[0096] Specifically, in this embodiment, the diagnosis results are calculated respectively The probability value includes two categories: Alzheimer's disease probability value and non-Alzheimer's disease probability value;

[0097] Determine the probability values of Alzheimer's disease and non-Alzheimer's disease in each diagnosis result, and vote for the category with the larger probability value;

[0098] Calculate the number of votes and take the classification with the largest number of votes as the output fusion diagnosis output result P.

[0099] like Figure 6 As shown, this embodiment also provides an Alzheimer's disease early warning analysis system based on multiple Chinese syllables, which is used to implement the Alzheimer's disease early warning analysis method based on multiple Chinese syllables described in the above embodiment. The system includes: a data perception module 1, a data preprocessing module 2, a classifier module 3, a multi-syllable fusion diagnosis module 4, and a warning screening and guidance suggestion module 5;

[0100] The data perception module 1 is used to collect the speech data of Alzheimer's patients and perform data perception to construct a polysyllabic sample;

[0101] The data preprocessing module 2 is used to preprocess the data of the polysyllabic sample using a multi-level graph embedding sparse feature learning method to construct a polysyllabic speech output feature;

[0102] The classifier module 3 is used to construct a classifier, and uses the cost-sensitive random configuration network in the classifier to optimize the multi-syllabic speech output features and output a cost-sensitive matrix;

[0103] The multi-syllable fusion diagnosis module 4 is used to construct a cost-sensitive weighted objective function based on the cost-sensitive matrix, calculate the multi-syllable fusion analysis diagnosis results of different classifiers, and warn the probability of Alzheimer's disease based on the diagnosis results;

[0104] The early warning screening and guidance suggestion module 5 is used to provide guidance and suggestions to patients based on the diagnosis results of the polysyllabic fusion diagnosis module 4.

[0105] In this embodiment, based on the patient data of a tertiary-level A hospital, 124 healthy elderly samples and 75 mild cognitive impairment patients were collected. The pronunciation combination of the above samples was collected. ,one ,Room The pronunciation data of the present invention can be used to assist in the diagnosis of mild cognitive impairment in the early stages of Alzheimer's disease with an accuracy rate of over 70%.

[0106] During the case implementation process, users use mobile phones and other platforms to enter syllables according to the guidance. After the entered syllables undergo data preprocessing, the classifier module and the multi-syllable fusion diagnosis module can obtain the probability of Alzheimer's disease under single syllables and the probability of Alzheimer's disease under multiple syllables, and can provide guidance such as precautions based on the probability.

[0107] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved, and this document is not limited here.

[0109] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for early warning analysis of Alzheimer's disease based on multiple Chinese syllables, characterized by: The following steps are involved: Collecting speech data of Alzheimer's patients and performing data perception to construct a polysyllabic sample; wherein the polysyllabic sample includes syllable pronunciation data; The multi-syllabic sample is preprocessed using a multi-level graph embedding sparse feature learning method to construct a multi-syllabic speech output feature, including: Extracting features from the syllable pronunciation data to obtain Mel-Cep coefficients, timing errors, and amplitude perturbation speech features, and performing normalization processing to obtain processed speech features; Constructing a graph random sparse autoencoder based on random configuration, and using the graph random sparse autoencoder to extract the processed speech features to obtain polysyllabic speech output features, including: Obtaining processed speech features, extracting the processed speech features using the graph random sparse autoencoder, and obtaining a hidden layer node output matrix of the graph random sparse autoencoder; Defining a graph random sparse autoencoder weight objective function, and taking the hidden layer node output matrix as input of the weight objective function, and outputting a graph random sparse autoencoder weight value; Combining the hidden layer node output matrix and the weight values, constructing a polysyllabic speech output feature; Constructing a classifier, using a cost-sensitive random configuration network in the classifier to perform feature optimization processing on the polysyllabic speech output features, and outputting a cost-sensitive matrix; A cost-sensitive weighted objective function is constructed based on the cost-sensitive matrix, and the multi-syllable fusion analysis diagnosis results of different classifiers are calculated respectively, and the probability of Alzheimer's disease is predicted according to the diagnosis results; Provide guidance and advice to the patient based on the Alzheimer's disease probability prediction results.

2. The Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to claim 1 is characterized in that: The process of collecting speech data of Alzheimer's patients and performing data perception to construct a polysyllabic sample is as follows: A recording sample is prepared, and the recording sample is annotated with Chinese characters, Chinese syllables, international phonetic symbols, and pronunciation prompts according to Chinese syllables. Alzheimer's patient voice data is collected based on the annotated results to obtain a polysyllabic sample.

3. The Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to claim 1 is characterized in that: The process of constructing a classifier, using a cost-sensitive random configuration network in the classifier to perform feature optimization processing on the polysyllabic speech output features, and outputting a cost-sensitive matrix is as follows: Initializing a cost-sensitive matrix based on the multi-syllabic speech output feature; A classifier is constructed using a cost-sensitive random configuration network based on an objective function, and the initialized cost-sensitive matrix is updated and outputted using the classifier and an intelligent optimization method to obtain a maximized objective function; The cost-sensitive matrix corresponding to the maximized objective function is used as an output result.

4. The Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to claim 3 is characterized in that: The maximization objective function is specifically: ； in, represents the objective function of classifier i, is the hidden layer output of classifier i, Represents the classification label, represents the classifier regularization parameter, is the cost-sensitive matrix, i is the number of classifiers, and N is the number of cost-sensitive matrices.

5. The Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to claim 4 is characterized in that: The process of constructing a cost-sensitive weighted objective function based on the cost-sensitive matrix, calculating the multi-syllable fusion analysis diagnosis results of different classifiers, and predicting the probability of Alzheimer's disease based on the diagnosis results is as follows: Constructing a weighted output matrix according to the cost-sensitive matrix and the hidden layer node output matrix; Constructing a cost-sensitive weighted objective function according to the weighted output matrix and the maximization objective function, and calculating a fusion output matrix according to the cost-sensitive weighted objective function; Calculating a diagnosis result based on the weighted output matrix and the fusion output matrix; The diagnostic results are analyzed using a voting method to obtain a fusion analysis diagnostic result.

6. The Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to claim 5 is characterized in that: The objective function based on cost-sensitive weighting is specifically: ； in, represents the original output weight of classifier k, is the weighted output matrix, specifically , is the weighted output matrix of classifier k, is the hidden layer output of classifier k, represents the regularization parameter of the fusion objective function, K is the number of classifiers, is the cost-sensitive matrix, B is the fusion output matrix, specifically , is the value of the cost-sensitive weighted objective function.

7. The Alzheimer's disease early warning analysis method based on multiple Chinese syllables according to claim 5 is characterized in that: The process of analyzing the diagnosis results by the voting method to obtain the fusion analysis diagnosis results is as follows: Calculating the probability values of the diagnosis results respectively, wherein the probability values include two categories: Alzheimer's disease probability value and non-Alzheimer's disease probability value; Determine the probability values of Alzheimer's disease and non-Alzheimer's disease in each diagnosis result, and vote for the category with the larger probability value; Count the votes and use the category with the most votes as the output value of the fusion analysis diagnosis result.

8. A multi-syllable-based Alzheimer's disease early warning analysis system, used to implement the multi-syllable-based Alzheimer's disease early warning analysis method according to any one of claims 1 to 7, characterized in that: The system comprises: a data perception module (1), a data preprocessing module (2), a classifier module (3), a multi-syllable fusion diagnosis module (4), and an early warning screening and guidance suggestion module (5); The data perception module (1) is used to collect speech data of Alzheimer's patients and perform data perception to construct a polysyllabic sample; The data preprocessing module (2) is used to perform data preprocessing on the polysyllabic sample by adopting a multi-level graph embedding sparse feature learning method to construct a polysyllabic speech output feature; The classifier module (3) is used to construct a classifier, and uses a cost-sensitive random configuration network in the classifier to perform feature optimization processing on the multi-syllabic speech output features, and outputs a cost-sensitive matrix; The polysyllabic fusion diagnosis module (4) is used to construct a cost-sensitive weighted objective function based on a cost-sensitive matrix, calculate the polysyllabic fusion analysis diagnosis results of different classifiers, and predict the probability of Alzheimer's disease based on the diagnosis results; The early warning screening and guidance suggestion module (5) is used to provide guidance and suggestions to the patient based on the diagnosis result of the polysyllabic fusion diagnosis module (4).

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